Catálogo de publicaciones - libros

Gene set enrichment analysis is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. In this paper we present a novel method integrating gene interaction information with Gene Ontology (GO) for the construction of new interesting enriched gene sets. The experimental results show that the introduced method improves over traditional methods that compute the enrichment of a single GO terms, i.e. that it is capable to find new statistically relevant descriptions of the biology governing the experiments not detectable by the existing methods.

Gene set enrichment analysis is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. In this paper we present a novel method integrating gene interaction information with Gene Ontology (GO) for the construction of new interesting enriched gene sets. The experimental results show that the introduced method improves over traditional methods that compute the enrichment of a single GO terms, i.e. that it is capable to find new statistically relevant descriptions of the biology governing the experiments not detectable by the existing methods.

Gene set enrichment analysis is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. In this paper we present a novel method integrating gene interaction information with Gene Ontology (GO) for the construction of new interesting enriched gene sets. The experimental results show that the introduced method improves over traditional methods that compute the enrichment of a single GO terms, i.e. that it is capable to find new statistically relevant descriptions of the biology governing the experiments not detectable by the existing methods.

Gene set enrichment analysis is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. In this paper we present a novel method integrating gene interaction information with Gene Ontology (GO) for the construction of new interesting enriched gene sets. The experimental results show that the introduced method improves over traditional methods that compute the enrichment of a single GO terms, i.e. that it is capable to find new statistically relevant descriptions of the biology governing the experiments not detectable by the existing methods.

Gene set enrichment analysis is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. In this paper we present a novel method integrating gene interaction information with Gene Ontology (GO) for the construction of new interesting enriched gene sets. The experimental results show that the introduced method improves over traditional methods that compute the enrichment of a single GO terms, i.e. that it is capable to find new statistically relevant descriptions of the biology governing the experiments not detectable by the existing methods.

Gene set enrichment analysis is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. In this paper we present a novel method integrating gene interaction information with Gene Ontology (GO) for the construction of new interesting enriched gene sets. The experimental results show that the introduced method improves over traditional methods that compute the enrichment of a single GO terms, i.e. that it is capable to find new statistically relevant descriptions of the biology governing the experiments not detectable by the existing methods.

Gene set enrichment analysis is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. In this paper we present a novel method integrating gene interaction information with Gene Ontology (GO) for the construction of new interesting enriched gene sets. The experimental results show that the introduced method improves over traditional methods that compute the enrichment of a single GO terms, i.e. that it is capable to find new statistically relevant descriptions of the biology governing the experiments not detectable by the existing methods.

Gene set enrichment analysis is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. In this paper we present a novel method integrating gene interaction information with Gene Ontology (GO) for the construction of new interesting enriched gene sets. The experimental results show that the introduced method improves over traditional methods that compute the enrichment of a single GO terms, i.e. that it is capable to find new statistically relevant descriptions of the biology governing the experiments not detectable by the existing methods.

Gene set enrichment analysis is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. In this paper we present a novel method integrating gene interaction information with Gene Ontology (GO) for the construction of new interesting enriched gene sets. The experimental results show that the introduced method improves over traditional methods that compute the enrichment of a single GO terms, i.e. that it is capable to find new statistically relevant descriptions of the biology governing the experiments not detectable by the existing methods.

Gene set enrichment analysis is a microarray data analysis method that uses predefined gene sets and ranks of genes to identify significant biological changes in microarray data sets. In this paper we present a novel method integrating gene interaction information with Gene Ontology (GO) for the construction of new interesting enriched gene sets. The experimental results show that the introduced method improves over traditional methods that compute the enrichment of a single GO terms, i.e. that it is capable to find new statistically relevant descriptions of the biology governing the experiments not detectable by the existing methods.